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In [1]: # import some dependencies import torch from torch.autograd import Variable import pyro import pyro.distributions as dist Models in Pyro: From Primitive Distributions to Stochastic Functions¶ <span>The basic unit of Pyro programs is the stochastic function. This is an arbitrary Python callable that combines two ingredients: deterministic Python code; and primitive stochastic functions Concretely, a stochastic function can be any Python

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Torch nn.Module . Throughout the tutorials and documentation, we will often call stochastic functions models, since stochastic functions can be used to represent simplified or abstract descriptions of a process by which data are generated. <span>Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ Primitive stochastic functions, or distrib

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s that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ <span>Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like

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the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like the bernoulli and uniform distributions, but <span>users can implement custom distributions by subclassing pyro.distributions.Distribution . Using primitive stochastic functions is easy. For example, to draw a sample x from the unit normal distribution (0,1)N(0,1) we do the following: In [2]: mu = Variable(torc

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users can implement custom distributions by subclassing pyro.distributions.Distribution .

the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like the bernoulli and uniform distributions, but <span>users can implement custom distributions by subclassing pyro.distributions.Distribution . Using primitive stochastic functions is easy. For example, to draw a sample x from the unit normal distribution (0,1)N(0,1) we do the following: In [2]: mu = Variable(torc

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Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables.

Torch nn.Module . Throughout the tutorials and documentation, we will often call stochastic functions models, since stochastic functions can be used to represent simplified or abstract descriptions of a process by which data are generated. <span>Expressing models as stochastic functions in Pyro means that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ Primitive stochastic functions, or distrib

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Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs.

s that models can be composed, reused, imported, and serialized just like regular Python callables. Without further ado, let’s introduce one of our basic building blocks: primitive stochastic functions. Primitive Stochastic Functions¶ <span>Primitive stochastic functions, or distributions, are an important class of stochastic functions for which we can explicitly compute the probability of the outputs given the inputs. Pyro includes a standalone library, pyro.distributions , of GPU-accelerated multivariate probability distributions built on PyTorch. This comes with various familiar distributions like

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30+ years Terms of Service Privacy Policy YOU ARE HERE: LAT Home→Collections Book review: 'The Thieves of Manhattan' by Adam Langer <span>Novelist Adam Langer skewers the publishing trade — and some of its recent trends — while digging toward something deeper. July 18, 2010|By Ella Taylor, Special to the Los Angeles Times Email Share The Thieves of Manhattan A Novel Adam Langer Spiegel & Grau: 260 pp., $15 paper

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dia, the free encyclopedia Jump to: navigation, search Not to be confused with Memorization. "Tabling" redirects here. For the parliamentary procedure, see Table (parliamentary procedure). <span>In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again. Memoization has also been used in other contexts (and for purposes other than speed gains), such as in simple mutually recursive descent parsing [1] . Although related to caching, memoi

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In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur a

dia, the free encyclopedia Jump to: navigation, search Not to be confused with Memorization. "Tabling" redirects here. For the parliamentary procedure, see Table (parliamentary procedure). <span>In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again. Memoization has also been used in other contexts (and for purposes other than speed gains), such as in simple mutually recursive descent parsing [1] . Although related to caching, memoi

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In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again.

dia, the free encyclopedia Jump to: navigation, search Not to be confused with Memorization. "Tabling" redirects here. For the parliamentary procedure, see Table (parliamentary procedure). <span>In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again. Memoization has also been used in other contexts (and for purposes other than speed gains), such as in simple mutually recursive descent parsing [1] . Although related to caching, memoi

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In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again.

dia, the free encyclopedia Jump to: navigation, search Not to be confused with Memorization. "Tabling" redirects here. For the parliamentary procedure, see Table (parliamentary procedure). <span>In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again. Memoization has also been used in other contexts (and for purposes other than speed gains), such as in simple mutually recursive descent parsing [1] . Although related to caching, memoi

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tion, one simply looks up the previously computed solution, thereby saving computation time at the expense of a (hopefully) modest expenditure in storage space. (Each of the subproblem solutions is indexed in some way, typically based on <span>the values of its input parameters, so as to facilitate its lookup.) <span><body><html>

This article's factual accuracy is disputed. Relevant discussion may be found on the talk page. Please help to ensure that disputed statements are reliably sourced. (November 2015) (Learn how and when to remove this template message) <span>In computer science, mathematics, management science, economics and bioinformatics, dynamic programming (also known as dynamic optimization) is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions. The next time the same subproblem occurs, instead of recomputing its solution, one simply looks up the previously computed solution, thereby saving computation time at the expense of a (hopefully) modest expenditure in storage space. (Each of the subproblem solutions is indexed in some way, typically based on the values of its input parameters, so as to facilitate its lookup.) The technique of storing solutions to subproblems instead of recomputing them is called "memoization". Dynamic programming algorithms are often used for optimization. A dyna

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Playing a central role in the theory of probability, the Wiener process is often considered the most important and studied stochastic process, with connections to other stochastic processes. [1] [2] [3] [78] [79] [80] [81] Its index set and state space are the non-negative numbers and real numbers, respectively, so it has both continuous index set and states space.

wnian motion due to its historical connection as a model for Brownian movement in liquids. [75] [76] [76] [77] [imagelink] Realizations of Wiener processes (or Brownian motion processes) with drift (blue) and without drift (red). <span>Playing a central role in the theory of probability, the Wiener process is often considered the most important and studied stochastic process, with connections to other stochastic processes. [1] [2] [3] [78] [79] [80] [81] Its index set and state space are the non-negative numbers and real numbers, respectively, so it has both continuous index set and states space. [82] But the process can be defined more generally so its state space can be n {\displaystyle n} -dimensional Euclidean space. [71] [79] [83]

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l Line integral Surface integral Volume integral Jacobian Hessian matrix Specialized[show] Fractional Malliavin Stochastic Variations Glossary of calculus[show] Glossary of calculus v t e <span>In mathematics, a Taylor series is a representation of a function as an infinite sum of terms that are calculated from the values of the function's derivatives at a single point. The concept of a Taylor series was formulated by the Scottish mathematician James Gregory and formally introduced by the English mathematician Brook Taylor in 1715. If the Taylor seri

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In mathematics, a Taylor series is a representation of a function as an infinite sum of terms that are calculated from the values of the function's derivatives at a single point.

l Line integral Surface integral Volume integral Jacobian Hessian matrix Specialized[show] Fractional Malliavin Stochastic Variations Glossary of calculus[show] Glossary of calculus v t e <span>In mathematics, a Taylor series is a representation of a function as an infinite sum of terms that are calculated from the values of the function's derivatives at a single point. The concept of a Taylor series was formulated by the Scottish mathematician James Gregory and formally introduced by the English mathematician Brook Taylor in 1715. If the Taylor seri

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U is open if every point in U has a neighborhood contained in U.

ntained in U. Metric spaces[edit] A subset U of a metric space (M, d) is called open if, given any point x in U, there exists a real number ε > 0 such that, given any point y in M with d(x, y) < ε, y also belongs to U. Equivalently, <span>U is open if every point in U has a neighborhood contained in U. This generalizes the Euclidean space example, since Euclidean space with the Euclidean distance is a metric space. Topological spaces[edit] In general topological spaces, the open

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if A then B expresses B only as a logical consequence of A; not necessarily a causal physical consequence

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if A then B expresses B only as a logical consequence of A; not necessarily a causal physical consequence